Method: FDG PET and SSM PCA

FDG PET

[18F]-fluoro-deoxyglucose positron emission tomography (FDG PET) imaging is an easy and stable radiotracer method validated extensively through the years. FDG is only in recent years commercially available and being used in all the larger Nuclear Medicine Departments worldwide. FDG has remained the only available radiotracer to detect accurately and reliably the cerebral glucose metabolism. As glucose is the only source of energy of the brain it reflects the energy needs of underlying brain neuronal systems. After being taken up by the brain, glucose or its radiotracer analogue [18F]-FDG, are phosphorylated by hexokinase to glucose-6-PO4 or fluorodeoxyglucose-6-PO4, the first step of the glycolytic process. However, FDG being a deoxy variant of glucose, is not a substrate for further metabolism and consequently trapped in brain tissue for the duration of the scanning procedure. Thus the outcome measure of regional cerebral FDG-uptake measured by FDG PET is the first step of the glycolysis. The detected disease-specific metabolic brain patterns using FDG PET are therefore reflecting the underlying pathological alterations of the affected brain regions. Specific brain regions degenerate and different patterns of altered glucose metabolic brain activity develop in various neurodegenerative brain diseases.

SSM PCA analysis

Scaled Subprofile Modelling/Principal Component Analysis (SSM/PCA) is a multivariate method, which allows us to identify disease-specific cerebral metabolic brain patterns in several neurodegenerative brain diseases at an early disease stage. The software we use is written in-house, based on the methods of Spetsieris and Eidelberg (Eidelberg 2009, Spetsieris and Eidelberg 2010). The method identifies not only group differences (as is the case in univariate methods), but is able to identify relationships in combined samples of patients and control scans (Eidelberg 2009). Covariance analysis techniques are considered appropriate methods to explore network activity. In the SSM, a threshold of the whole-brain maximum is applied to remove out-of-brain voxels, followed by a log transformation. A threshold of 35% is used, resulting in a mask of mainly grey matter (Spetsieris and Eidelberg 2010). After removing between-subject and between-region averages, a principal component analysis (PCA) is applied. PCA transforms a set of correlated variables into a new set of orthogonal uncorrelated variables that are called the principal components. Voxels participating in each principal component (PC) may have either a positive or a negative loading. The loadings express the covariance structure (i.e. the strength of the interaction) between the voxels that participate in het PC. They are ordered in terms of the variability they represent. That is, the first principal component represents for a single dimension (variable) the greatest amount of variability in the original dataset. Each succeeding orthogonal component accounts for as much of the remaining variability as possible. They can be very helpful in determining how many of the components are really significant and how much the data can be reduced.

The components that together describe at least 50% of the variance are used for further analysis. This is an arbitrary limit, but is used in most studies. To identify a covariance pattern that best discriminates a patient group form a control group, each subject’s expression of the selected principal components with the lowest AIC (Akaike Information Criterion) value (Akaike 1974) are entered into a stepwise regression procedure. This regression results in a linear combination of the PCs that best discriminate the two groups and is designated as the disease-specific metabolic covariance pattern.

This metabolic covariance pattern can be applied to individual patients to test whether they express the pattern or not. Every voxel value in a subject scan is multiplied by the corresponding voxel weight in the covariance pattern with a subsequent summation over the whole brain. The resulting subject score captures to what extent a subject expresses the covariance pattern.

Case 1:

A woman of 42 years old came for a second opinion. Since two years she is suffering from fatigue, slowness, stiffness in the limbs more left than right. At neurological examination she had a high score (45 points) on the Unified Parkinson’s Disease part 3. Under the suspicion of Parkinson’s disease her levodopa medication was increased. After 4 years and a moderate effect of levodopa and apomorfine, she had an accelerated progression of her symptoms: she had difficulty swallowing, urine incontinence and wheel chair bound. The physician doubted about the initial diagnosis and therefore requested a FDG PET scan with the following question: Are there metabolic deficits compatible with MSA?

FDG PET scan

SSM/PCA method:

Subjectscore of 1 patient (HR+ scan) compared with the

MSA, PSP, PD and AD disease-related metabolic pattern

Outcome visual examination of the FDG PET scan:

Low FDG uptake in putamen and cerebellum compatible with MSA

Z-scores calculated with the SSM-PCA method:

increased expression of the MSA disease-related pattern

Conclusion: MSA

Case 2:

A man of 72 years came to the outpatient department. Since 7 years he had progressive problems with memory, walking and balance and general slowness. At neurological examination he had a mask face, vertical gaze palsy, rigidity bilaterally and axially, bradykinesia and frontal execution dysfunction. The physician suspected the patient of having PSP. To strengthen this diagnosis he requested a FDG PET scan with the following question: Is there mediofrontal hypometabolism compatible with PSP?